Statistical learning theory

Материал из Wiki - Факультет компьютерных наук
Перейти к: навигация, поиск

General Information

Syllabus for the 1st module

Course materials

Date Summary Lecture notes Problem list
5 sept PAC-learning and VC-dimension: definitions 1st and 2nd lecture Updated on 13th of Sept. Problem list 1
12 sept PAC-learning and VC-dimension: proof of fundamental theorem Problem list 2

19 sept Sauer's lemma, agnostic PAC-learning, structural risk minimization 3th lecture Only the first part of the notes.
26 sept Computational learning theory

3 okt Boosting: the adaBoost algorithm
10 okt Boosting: several other algorithms
17 okt Online learning algorithms

A gentle introduction to the materials of the first 3 lectures and an overview of probability theory, can be found in chapters 1-6 and 11-12 of the following book: Sanjeev Kulkarni and Gilbert Harman: An Elementary Introduction to Statistical Learning Theory, 2012.

Office hours

Person Monday Tuesday Wednesday Thursday Friday
Bruno Bauwens 15:05–18:00 15:05–18:00 Room 620
Quentin Paris